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User Modeling and User-Adapted Interaction 22

Editors:Alfred Kobsa
Standard No:ISSN 0924-1868 (print) EISSN 1573-1391 (online)
Links:link.springer.com | Table of Contents
  1. UMUAI 2012-04 Volume 22 Issue 1/2
  2. UMUAI 2012-07 Volume 22 Issue 3
  3. UMUAI 2012-10 Volume 22 Issue 4/5

UMUAI 2012-04 Volume 22 Issue 1/2

Special Issue on Coming of Age: Celebrating a Quarter Century of User Modeling and Personalization

Coming of age: celebrating a quarter century of user modeling and personalization: Guest editors' introduction BIBFull-Text 1-7
  Judy Kay; Gord McCalla
A review of recent advances in learner and skill modeling in intelligent learning environments BIBAKFull-Text 9-38
  Michel C. Desmarais; Ryan S. J. d. Baker
In recent years, learner models have emerged from the research laboratory and research classrooms into the wider world. Learner models are now embedded in real world applications which can claim to have thousands, or even hundreds of thousands, of users. Probabilistic models for skill assessment are playing a key role in these advanced learning environments. In this paper, we review the learner models that have played the largest roles in the success of these learning environments, and also the latest advances in the modeling and assessment of learner skills. We conclude by discussing related advancements in modeling other key constructs such as learner motivation, emotional and attentional state, meta-cognition and self-regulated learning, group learning, and the recent movement towards open and shared learner models.
Keywords: Student models; Learner models; Probabilistic models; Bayesian Networks; IRT; Model tracing; POKS; Bayesian Knowledge Tracing; Intelligent Tutoring System; Learning environments; Cognitive modeling
Fifteen years of constraint-based tutors: what we have achieved and where we are going BIBAKFull-Text 39-72
  Antonija Mitrovic
Fifteen years ago, research started on SQL-Tutor, the first constraint-based tutor. The initial efforts were focused on evaluating Constraint-Based Modeling (CBM), its effectiveness and applicability to various instructional domains. Since then, we extended CBM in a number of ways, and developed many constraint-based tutors. Our tutors teach both well- and ill-defined domains and tasks, and deal with domain- and meta-level skills. We have supported mainly individual learning, but also the acquisition of collaborative skills. Authoring support for constraint-based tutors is now available, as well as mature, well-tested deployment environments. Our current research focuses on building affect-sensitive and motivational tutors. Over the period of fifteen years, CBM has progressed from a theoretical idea to a mature, reliable and effective methodology for developing effective tutors.
Keywords: Constraint-based modeling; Constraint-based tutors; Authoring; Affective modeling; Metacognitive skills; Collaborative learning
Personalization in cultural heritage: the road travelled and the one ahead BIBAKFull-Text 73-99
  Liliana Ardissono; Tsvi Kuflik
Over the last 20 years, cultural heritage has been a favored domain for personalization research. For years, researchers have experimented with the cutting edge technology of the day; now, with the convergence of internet and wireless technology, and the increasing adoption of the Web as a platform for the publication of information, the visitor is able to exploit cultural heritage material before, during and after the visit, having different goals and requirements in each phase. However, cultural heritage sites have a huge amount of information to present, which must be filtered and personalized in order to enable the individual user to easily access it. Personalization of cultural heritage information requires a system that is able to model the user (e.g., interest, knowledge and other personal characteristics), as well as contextual aspects, select the most appropriate content, and deliver it in the most suitable way. It should be noted that achieving this result is extremely challenging in the case of first-time users, such as tourists who visit a cultural heritage site for the first time (and maybe the only time in their life). In addition, as tourism is a social activity, adapting to the individual is not enough because groups and communities have to be modeled and supported as well, taking into account their mutual interests, previous mutual experience, and requirements. How to model and represent the user(s) and the context of the visit and how to reason with regard to the information that is available are the challenges faced by researchers in personalization of cultural heritage. Notwithstanding the effort invested so far, a definite solution is far from being reached, mainly because new technology and new aspects of personalization are constantly being introduced. This article surveys the research in this area. Starting from the earlier systems, which presented cultural heritage information in kiosks, it summarizes the evolution of personalization techniques in museum web sites, virtual collections and mobile guides, until recent extension of cultural heritage toward the semantic and social web. The paper concludes with current challenges and points out areas where future research is needed.
Keywords: Personalized access to cultural heritage; Personalization; Cultural heritage
Recommender systems: from algorithms to user experience BIBAKFull-Text 101-123
  Joseph A. Konstan; John Riedl
Since their introduction in the early 1990's, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in collaborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of questions around the user experience with the recommender. We show through examples that the embedding of the algorithm in the user experience dramatically affects the value to the user of the recommender. We argue that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and suggest additional measures that have proven effective. Based on our analysis of the state of the field, we identify the most important open research problems, and outline key challenges slowing the advance of the state of the art, and in some cases limiting the relevance of research to real-world applications.
Keywords: Recommender systems; User experience; Collaborative filtering; Evaluation; Metrics
Critiquing-based recommenders: survey and emerging trends BIBAKFull-Text 125-150
  Li Chen; Pearl Pu
Critiquing-based recommender systems elicit users' feedback, called critiques, which they made on the recommended items. This conversational style of interaction is in contract to the standard model where users receive recommendations in a single interaction. Through the use of the critiquing feedback, the recommender systems are able to more accurately learn the users' profiles, and therefore suggest better recommendations in the subsequent rounds. Critiquing-based recommenders have been widely studied in knowledge-, content-, and preference-based recommenders and are beginning to be tried in several online websites, such as MovieLens. This article examines the motivation and development of the subject area, and offers a detailed survey of the state of the art concerning the design of critiquing interfaces and development of algorithms for critiquing generation. With the help of categorization analysis, the survey reveals three principal branches of critiquing based recommender systems, using respectively natural language based, system-suggested, and user-initiated critiques. Representative example systems will be presented and analyzed for each branch, and their respective pros and cons will be discussed. Subsequently, a hybrid framework is developed to unify the advantages of different methods and overcome their respective limitations. Empirical findings from user studies are further presented, indicating how hybrid critiquing supports could effectively enable end-users to achieve more confident decisions. Finally, the article will point out several future trends to boost the advance of critiquing-based recommenders.
Keywords: Critiquing-based recommenders; Survey; Preference elicitation; Example critiquing; Dynamic critiquing; Hybrid critiquing; User evaluations
Discovery of Web user communities and their role in personalization BIBAKFull-Text 151-175
  Georgios Paliouras
One of the major innovations in personalization in the last 20 years was the injection of social knowledge into the model of the user. The user is not considered an isolated individual any more, but a member of one or more communities. User communities have been facilitated by the striking advancements of electronic communications and in particular the penetration of the Web into people's everyday routine. Communities arise in a number of different ways. Social networking tools typically allow users to proactively connect to each other. Alternatively, data mining tools discover communities of connected Web sites or communities of Web users. In this article, we focus on the latter type of community, which is commonly mined from logs of users' activity on the Web. We recall how this process has been used to model the users' interests and personalize Web applications. Collaborative filtering and recommendation are the most widely used forms of community-driven personalization. However, we examine a range of other interesting alternatives that are worth investigating further. This effort leads us naturally to the recent developments on the Web and particularly the advent of the social Web. We explain how this development draws together the different viewpoints on Web communities and introduces new opportunities for community-based personalization. In particular, we propose the concept of active user community and show how this relates to recent efforts on mining social networks and social media.
Keywords: User communities; Web mining; Web personalization; Web communities; Social networks
Motivating participation in social computing applications: a user modeling perspective BIBAKFull-Text 177-201
  Julita Vassileva
The explosive growth of Web-based social applications over the last 10 years has led people to engage in online communities for various purposes: to work, learn, play, share time and mementos with friends and family and engage in public action. Social Computing Applications (SCA) allow users to discuss various topics in online forums, share their thoughts in blogs, share photos, videos, bookmarks, and connect with friends through social networks. Yet, the design of successful social applications that attract and sustain active contribution by their users still remains more of an art than a science. My research over the last 10 years has been based on the hypothesis that it is possible to incorporate mechanisms and tools in the design of the social application that can motivate users to participate, and more generally, to change their behavior in a desirable way, which is beneficial for the community. Since different people are motivated by different things, it can be expected that personalizing the incentives and the way the rewards are presented to the individual, would be beneficial. Also since communities have different needs in different phases of their existence, it is necessary to model the changing needs of communities and adapt the incentive mechanisms accordingly, to attract the kind of contributions that are beneficial. Therefore User and Group (Community) Modeling is an important area in the design of incentive mechanisms. This paper presents an overview of different approaches to motivate users to participate. These approaches are based on various theories from the area of social psychology and behavioral economics and involve rewards mechanisms, reputation, open group user modeling, and social visualization. Future trends are outlined towards convergence with the areas of persuasive systems design, adaptive/personalized systems, and intelligent social learning environments.
Keywords: Social computing; Participation; Motivation; Persuasion; Gamification; Open user models; Group user models; Reflection; Adaptive incentive mechanism; Incentives; Mechanism design
Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems BIBAKFull-Text 203-220
  Eran Toch; Yang Wang; Lorrie Faith Cranor
Personalization technologies offer powerful tools for enhancing the user experience in a wide variety of systems, but at the same time raise new privacy concerns. For example, systems that personalize advertisements according to the physical location of the user or according to the user's friends' search history, introduce new privacy risks that may discourage wide adoption of personalization technologies. This article analyzes the privacy risks associated with several current and prominent personalization trends, namely social-based personalization, behavioral profiling, and location-based personalization. We survey user attitudes towards privacy and personalization, as well as technologies that can help reduce privacy risks. We conclude with a discussion that frames risks and technical solutions in the intersection between personalization and privacy, as well as areas for further investigation. This frameworks can help designers and researchers to contextualize privacy challenges of solutions when designing personalization systems.
Keywords: Privacy; Personalization; Human--computer interaction; Social networks; E-commerce; Location-based services

UMUAI 2012-07 Volume 22 Issue 3

Context-dependent awareness support in open collaboration environments BIBAKFull-Text 223-254
  Liliana Ardissono; Gianni Bosio
The widespread adoption of online services for performing work, home and leisure tasks enables users to operate in the ubiquitous environment provided by the Internet by managing a possibly high number of parallel (private and shared) activity contexts. The provision of awareness information is a key factor for keeping users up-to-date with what happens around them; e.g., with the operations performed by their collaborators. However, the delivery of notifications describing the occurred events can interrupt the users' activities, with a possible disruptive effect on their emotional and attentional states. As a possible solution to the trade-off between informing and interrupting users, we defined two context-dependent notification management policies which support the selection of the notifications to be delivered on the basis of the user's current activities, at different granularity levels: general collaboration context versus task carried out. These policies are offered by the COntext depeNdent awaReness informAtion Delivery (CONRAD) framework. We tested such policies with users by applying them in a collaboration environment that includes a set of largely used Web 2.0 services. The experiments show that our policies reduce the levels of workload on users while supporting an up-to-the-moment understanding of the interaction with their shared contexts. The present paper presents the CONRAD framework and the techniques underlying the proposed notification policies.
Keywords: Personalized awareness information support; Notification management policies; Interruption management; Collaboration environments; Context awareness; Web 2.0
Tune in to your emotions: a robust personalized affective music player BIBAKFull-Text 255-279
  Joris H. Janssen; Egon L. van den Broek
The emotional power of music is exploited in a personalized affective music player (AMP) that selects music for mood enhancement. A biosignal approach is used to measure listeners' personal emotional reactions to their own music as input for affective user models. Regression and kernel density estimation are applied to model the physiological changes the music elicits. Using these models, personalized music selections based on an affective goal state can be made. The AMP was validated in real-world trials over the course of several weeks. Results show that our models can cope with noisy situations and handle large inter-individual differences in the music domain. The AMP augments music listening where its techniques enable automated affect guidance. Our approach provides valuable insights for affective computing and user modeling, for which the AMP is a suitable carrier application.
Keywords: Mood; Music; Psychophysiology; User modeling; Kernel density estimation; Validation; Affective computing
Modeling sequences of user actions for statistical goal recognition BIBAKFull-Text 281-311
  Marcelo G. Armentano; Analía A. Amandi
User goals are of major importance for an interface agent because they serve as a context to define what the user's focus of attention is at a given moment. The user's goals should be detected as soon as possible, after observing few user actions, in order to provide the user with timely assistance. In this article, we describe an approach for modeling and recognizing user goals from observed sequences of user actions by using Variable Order Markov models combined with an exponential moving average (EMA) on the prediction probabilities. The validity of our approach has been tested using data collected from real users in the Unix domain. The results obtained show that an interface agent can achieve near 90% average accuracy and over 58% online accuracy in predicting the most probable user goal after each observed action, in a time linear to the number of goals being modeled. We also found that the use of an EMA allows a faster convergence in the actual user goal.
Keywords: Goal recognition; Variable Order Markov models; Interface agents; User modeling

UMUAI 2012-10 Volume 22 Issue 4/5

Special Issue on User Interfaces for Recommender Systems

Preface to the special issue on user interfaces for recommender systems BIBFull-Text 313-316
  Alexander Felfernig; Robin Burke; Pearl Pu
Evaluating recommender systems from the user's perspective: survey of the state of the art BIBAKFull-Text 317-355
  Pearl Pu; Li Chen; Rong Hu
A recommender system is a Web technology that proactively suggests items of interest to users based on their objective behavior or explicitly stated preferences. Evaluations of recommender systems (RS) have traditionally focused on the performance of algorithms. However, many researchers have recently started investigating system effectiveness and evaluation criteria from users' perspectives. In this paper, we survey the state of the art of user experience research in RS by examining how researchers have evaluated design methods that augment RS's ability to help users find the information or product that they truly prefer, interact with ease with the system, and form trust with RS through system transparency, control and privacy preserving mechanisms finally, we examine how these system design features influence users' adoption of the technology. We summarize existing work concerning three crucial interaction activities between the user and the system: the initial preference elicitation process, the preference refinement process, and the presentation of the system's recommendation results. Additionally, we will also cover recent evaluation frameworks that measure a recommender system's overall perceptive qualities and how these qualities influence users' behavioral intentions. The key results are summarized in a set of design guidelines that can provide useful suggestions to scholars and practitioners concerning the design and development of effective recommender systems. The survey also lays groundwork for researchers to pursue future topics that have not been covered by existing methods.
Keywords: Research survey; Recommender systems; User experience research; Explanation interface; Design guidelines
Designing interfaces for explicit preference elicitation: a user-centered investigation of preference representation and elicitation process BIBAKFull-Text 357-397
  Alina Pommeranz; Joost Broekens
Two problems may arise when an intelligent (recommender) system elicits users' preferences. First, there may be a mismatch between the quantitative preference representations in most preference models and the users' mental preference models. Giving exact numbers, e.g., such as "I like 30 days of vacation 2.5 times better than 28 days" is difficult for people. Second, the elicitation process can greatly influence the acquired model (e.g., people may prefer different options based on whether a choice is represented as a loss or gain). We explored these issues in three studies. In the first experiment we presented users with different preference elicitation methods and found that cognitively less demanding methods were perceived low in effort and high in liking. However, for methods enabling users to be more expressive, the perceived effort was not an indicator of how much the methods were liked. We thus hypothesized that users are willing to spend more effort if the feedback mechanism enables them to be more expressive. We examined this hypothesis in two follow-up studies. In the second experiment, we explored the trade-off between giving detailed preference feedback and effort. We found that familiarity with and opinion about an item are important factors mediating this trade-off. Additionally, affective feedback was preferred over a finer grained one-dimensional rating scale for giving additional detail. In the third study, we explored the influence of the interface on the elicitation process in a participatory set-up. People considered it helpful to be able to explore the link between their interests, preferences and the desirability of outcomes. We also confirmed that people do not want to spend additional effort in cases where it seemed unnecessary. Based on the findings, we propose four design guidelines to foster interface design of preference elicitation from a user view.
Keywords: Preference elicitation; Constructive preferences; Interface design
Evaluating the effectiveness of explanations for recommender systems BIBAKFull-Text 399-439
  Nava Tintarev; Judith Masthoff
When recommender systems present items, these can be accompanied by explanatory information. Such explanations can serve seven aims: effectiveness, satisfaction, transparency, scrutability, trust, persuasiveness, and efficiency. These aims can be incompatible, so any evaluation needs to state which aim is being investigated and use appropriate metrics. This paper focuses particularly on effectiveness (helping users to make good decisions) and its trade-off with satisfaction. It provides an overview of existing work on evaluating effectiveness and the metrics used. It also highlights the limitations of the existing effectiveness metrics, in particular the effects of under- and overestimation and recommendation domain. In addition to this methodological contribution, the paper presents four empirical studies in two domains: movies and cameras. These studies investigate the impact of personalizing simple feature-based explanations on effectiveness and satisfaction. Both approximated and real effectiveness is investigated. Contrary to expectation, personalization was detrimental to effectiveness, though it may improve user satisfaction. The studies also highlighted the importance of considering opt-out rates and the underlying rating distribution when evaluating effectiveness.
Keywords: Recommender systems; Metrics; Item descriptions; Explanations; Empirical studies
Explaining the user experience of recommender systems BIBAKFull-Text 441-504
  Bart P. Knijnenburg; Martijn C. Willemsen
Research on recommender systems typically focuses on the accuracy of prediction algorithms. Because accuracy only partially constitutes the user experience of a recommender system, this paper proposes a framework that takes a user-centric approach to recommender system evaluation. The framework links objective system aspects to objective user behavior through a series of perceptual and evaluative constructs (called subjective system aspects and experience, respectively). Furthermore, it incorporates the influence of personal and situational characteristics on the user experience. This paper reviews how current literature maps to the framework and identifies several gaps in existing work. Consequently, the framework is validated with four field trials and two controlled experiments and analyzed using Structural Equation Modeling. The results of these studies show that subjective system aspects and experience variables are invaluable in explaining why and how the user experience of recommender systems comes about. In all studies we observe that perceptions of recommendation quality and/or variety are important mediators in predicting the effects of objective system aspects on the three components of user experience: process (e.g. perceived effort, difficulty), system (e.g. perceived system effectiveness) and outcome (e.g. choice satisfaction). Furthermore, we find that these subjective aspects have strong and sometimes interesting behavioral correlates (e.g. reduced browsing indicates higher system effectiveness). They also show several tradeoffs between system aspects and personal and situational characteristics (e.g. the amount of preference feedback users provide is a tradeoff between perceived system usefulness and privacy concerns). These results, as well as the validated framework itself, provide a platform for future research on the user-centric evaluation of recommender systems.
Keywords: Recommender systems; Decision support systems; User experience; User-centric evaluation; Decision-making; Human-computer interaction; User testing; Preference elicitation; Privacy